Nisansa Dilushan de Silva

I am a lecturer at the Department of Computer Science & Engineering, University of Moratuwa, Sri Lanka. I obtained my Ph.D (2020, advisor: Dejing Dou) and MS (2016) degrees in Computer and Information Science from University of Oregon, USA and my BSc (Hons) in Computer Science & Engineering (2011) degree from University of Moratuwa. I joined University of Moratuwa as a lecturer in 2011. From 2013 to 2014, I worked as a researcher at LIRNEasia. In 2018, I worked as a Givens Associate at Argonne National Laboratory, USA. I have over 40 peer reviewed publications in the field of computer science, mostly under the subfields of Natural Language Processing and Artificial Intelligence, earning more than 400 citations. I am also an associate member of the IESL.

Professional Experience

  • Present 2020

    Senior Lecturer

    Department of Computer science & Engineering, University of Moratuwa,
    Sri Lanka

  • 2021 2020

    Research Fellow

    LIRNEasia,
    Sri Lanka

  • 2020 2014

    Graduate Research/Teaching Fellow

    University of Oregon, Department of Computer and Information Science,
    USA.

  • 2018 2018

    Givens Associate

    Argonne National Laboratory,
    USA.

  • 2020 2011

    Lecturer

    Department of Computer science & Engineering, University of Moratuwa,
    Sri Lanka

  • 2014 2013

    Researcher

    LIRNEasia,
    Sri Lanka

  • 2014 2013

    Visiting Lecturer

    Northshore College of Business and Technology,
    Sri Lanka

Education

  • Ph.D. 2020

    Ph.D. in Computer & Information Science

    University of Oregon, USA

  • MS 2016

    MS in Computer & Information Science

    University of Oregon, USA

  • BSc2011

    B.Sc Engineering (Hons)in Computer Science & Engineering

    University of Moratuwa, Sri Lanka

Featured Research

Enabling Effective Synoptic Assessment via Algorithmic Constitution of Review Panels


N. H. Nisansa Dilushan de Silva, S. Weerawarana, and A. Perera

Teaching, Assessment and Learning for Engineering (TALE), 2013 IEEE International Conference on, IEEE, Aug. 2013, pp. 776--781,

This paper presents an algorithmic tool that was used to create panels of experts for the synoptic assessment of a software engineering project course that is targeted towards fostering innovation and creativity in software engineering students. Synoptic assessments succeed with the ability to formulate expert evaluation panels. Yet many industry experts are busy professionals, and hence, the process of constituting appropriately balanced evaluation panels for project demonstrations is a significant challenge. The discussion includes the outcomes of using the algorithm to automate the panel composition and scheduling process for synoptic assessments of project demonstrations for a batch of 100 students following the Bachelor of Engineering (Honors) degree program in the Department of Computer Science and Engineering at the University of Moratuwa in Sri Lanka. We describe the challenges we faced and our approach towards addressing the issues as well as our encouraging successes.